A Design for Real-time Neural Modeling on the GPU Incorporating Dendritic Computation

Tyler W. Garaas, Frank Marino, Halil Duzcu, Marc Pomplun

Abstract

Recent advances in neuroscience have underscored the role of single neurons in information processing. Much of this work has focused on the role of neurons' dendrites to perform complex local computations that form the basis for the global computation of the neuron. Generally, artificial neural networks that are capable of real-time simulation do not take into account the principles underlying single-neuron processing. In this paper we propose a design for a neural model executed on the graphics processing unit (GPU) that is capable of simulating large neural networks that utilize dendritic computation inspired by biological neurons. We subsequently test our design using a neural model of the retinal neurons that contribute to the activation of starburst amacrine cells, which, as in biological retinas, use dendritic computational abilities to produce a neural signal that is directionally selective to stimuli moving centrifugally.

References

  1. Sejnowski, T. J., Koch, C. & Churchland, P. S. (1988). Computational neuroscience. Science, 241, 1299-1306.
  2. Koch, C. & Segev, I. (2000). The role of single neurons in information processing. Nature Neuroscience, 3, 1171-1177.
  3. Gray, C. M., König, P., Engel, A. K. & Singer, W. (1989). Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature, 338, 334-347.
  4. Vaadia, E., Haalman, I., Abeles, M., Bergman, H., Prut, Y., Slovin, H. & Aertsen, A. (1995). Dynamics of neuronal interactions in monkey cortex in relation to behavioural events. Nature, 373, 515-518.
  5. London, M. & Häusser M. (2005). Dendritic computation. Annual Review of Neuroscience, 28, 503-532.
  6. Agmon-Snir, H., Carr, C. E. & Rinzel, J. (1998). The role of dendrites in auditory coincidence detection. Nature, 393, 268-272.
  7. Mainen, Z. F. & Sejnowski, T. J. (1996). Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382, 363-366.
  8. Segev, I & Rall, W. (1988). Computational study of an excitable dendritic spine. Journal of Neurophysiology, 60, 499-523.
  9. Euler, T., Detwiler, P. B. & Denk, W. (2002). Directionally selective calcium signals in dendrites of starburst amacrine cells. Nature, 418, 845-852.
  10. Tukker, J. J., Taylor, W. R. & Smith, R. G. (2004). Direction selectivity in a model of the starburst amacrine cell. Visual Neuroscience, 21, 611-625.
  11. Owens, J. D., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A. E. & Purcell, T. J. (2007). A survey of general-purpose computation on graphics hardware. Computer Graphics Forum, 26, 80-113.
  12. GPGPU. Retrieved April 01, 2009 from http://www.gpgpu.org
  13. Bernhard, F. & Keriven, R. (2006). Spiking Neurons on GPUs. International Conference on Computation Science. Workshop general purpose computation on graphics hardware (GPGPU): Methods algorithms and applications, Readings, U.K.
  14. Gobron, S., Devillard, F. & Heit, B. (2007). Retina simulation using cellular automata and GPU programming. Machine Vision and Applications, 18, 331-342.
  15. Woodbeck, K., Roth, G. & Chen, H. (2008). Visual cortex on the GPU: Biologically inspired classifier and feature descriptor for rapid recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, U.S.A.
  16. Dacey, D. M. (2000). Parallel pathways for spectral coding in primate retina. Annual Review of Neuroscience, 23, 743-775.
  17. Dowling, J. E. (1987). The Retina: An Approachable Part of the Brain. Cambridge, MA, USA. Belknap Press.
  18. Garaas, T. W. & Pomplun, M. (2007). Retina-inspired visual processing. Proceedings of BIONETICS, Workshop on Computing and Communications from Biological Systems: Theory and Applications (CCBS). Budapest, Hungary.
  19. Blake, R., Sekuler, R., & Grossman, E. (2003). Motion processing in human visual cortex. In J H Kaas and C E Collins (Eds.), The Primate Visual System. Boca Raton: CRC Press.
  20. Mel, B. W., Ruderman, D. L., & Archie, K. A. (1998). Translation-invariant orientation tuning in visual “complex” cells could derive from intradendritic computations. The Journal of Neuroscience, 18, 4325-4334.
  21. Barlow, H. (1996). Intraneuronal information processing, directional selectivity and memory for spatio-temporal sequences. Network, 7, 251-259.
Download


Paper Citation


in Harvard Style

Garaas T., Marino F., Duzcu H. and Pomplun M. (2009). A Design for Real-time Neural Modeling on the GPU Incorporating Dendritic Computation . In Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: Workshop ANNIIP, (ICINCO 2009) ISBN 978-989-674-002-3, pages 69-78. DOI: 10.5220/0002265100690078


in Bibtex Style

@conference{workshop anniip09,
author={Tyler W. Garaas and Frank Marino and Halil Duzcu and Marc Pomplun},
title={A Design for Real-time Neural Modeling on the GPU Incorporating Dendritic Computation},
booktitle={Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: Workshop ANNIIP, (ICINCO 2009)},
year={2009},
pages={69-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002265100690078},
isbn={978-989-674-002-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: Workshop ANNIIP, (ICINCO 2009)
TI - A Design for Real-time Neural Modeling on the GPU Incorporating Dendritic Computation
SN - 978-989-674-002-3
AU - Garaas T.
AU - Marino F.
AU - Duzcu H.
AU - Pomplun M.
PY - 2009
SP - 69
EP - 78
DO - 10.5220/0002265100690078